The use of linkage disequilibrium (LD) in detecting disease genes has recently drawn much attention in genetic epidemiology. LD is evaluated with association analysis, which, when applied to disease-gene mapping, requires the comparison of allele or haplotype frequencies between the affected and the control individuals, under the assumption that a reasonable proportion of disease-associated chromosomes has been derived from a common ancestor. Traditional association analysis methods have long been used to test the involvement of candidate genes in diseases and, in special circumstances, to fine-map disease loci found by linkage methods. The testing has mostly been done using simple two-point measures.
Improved statistical methods to detect LD have been presented lately (Terwilliger 1995; Devlin et al. 1996; Lazzeroni 1998; McPeek and Strahs 1999; Service et al. 1999). The newer methods are based on statistical models of LD around a disease susceptibility (DS) gene. Genomic regions—rather than alleles—that are shared among affected individuals, are searched for. The recombination history from the common ancestor to the present day is taken into account with more or less simplified statistical models. The power of these methods, as well as their ability to localize the correct position of the DS gene, has been shown to be better than that of traditional methods. Some of the models are robust to high levels of etiologic heterogeneity (McPeek and Strahs 1999; Service et al. 1999). However, the methods contain assumptions about the inheritance model of the disease and the structure of the survey population, and the effects of violations of these assumptions in the real data are not known. In addition, they can only consider association of one region at a time. Thus, they are currently best suited for fine mapping, rather than complex disease mapping or genome screening. The methods also tend to be computationally heavy.